Int. J. Environ. Res. Public Health 2022, 19, 11881. https://doi.org/10.3390/ijerph191911881 www.mdpi.com/journal/ijerph
Influence of the Demographic, Social, and Environmental
Factors on the COVID-19 Pandemic—Analysis of the Local
Variations Using Geographically Weighted Regression
Krzysztof Rząsa and Mateusz Ciski *
Faculty of Geoengineering, Institute of Spatial Management and Geography, Department of Land
Management and Geographic Information Systems, University of Warmia and Mazury in Olsztyn,
10-720 Olsztyn, Poland
* Correspondence: email@example.com
Abstract: As the COVID-19 pandemic continues, an increasing number of different research studies
focusing on various aspects of the pandemic are emerging. Most of the studies focus on the medical
aspects of the pandemic, as well as on the impact of COVID-19 on various areas of life; less emphasis
is put on analyzing the influence of socio-environmental factors on the spread of the pandemic. In
this paper, using the geographically weighted regression method, the extent to which demographic,
social, and environmental factors explain the number of cases of SARS-CoV-2 is explored. The re-
search was performed for the case-study area of Poland, considering the administrative division of
the country into counties. The results showed that the demographic factors best explained the num-
ber of cases of SARS-CoV-2; the social factors explained it to a medium degree; and the environ-
mental factors explained it to the lowest degree. Urban population and the associated higher
amount and intensity of human contact are the most influential factors in the development of the
COVID-19 pandemic. The analysis of the factors related to the areas burdened by social problems
resulting primarily from the economic exclusion revealed that poverty-burdened areas are highly
vulnerable to the development of the COVID-19 pandemic. Using maps of the local R2 it was possi-
ble to visualize how the relationships between the explanatory variables (for this research—demo-
graphic, social, and environmental factors) and the dependent variable (number of cases of SARS-
CoV-2) vary across the study area. Through the GWR method, counties were identified as particu-
larly vulnerable to the pandemic because of the problem of economic exclusion. Considering that
the COVID-19 pandemic is still ongoing, the results obtained may be useful for local authorities in
developing strategies to counter the pandemic.
Keywords: COVID-19; SARS-CoV-2; pandemic; geographically weighted regression; GWR;
geographic information system; GIS
In December 2019, the Chinese government reported the first cases of the SARS-CoV-
2 infection in Wuhan, the capital of Hubei province [1,2]. The epidemic quickly spread to
all the provinces in China. More cases began to appear in countries around the world. On
11 March 2020, the World Health Organization declared this coronavirus outbreak a pan-
demic . The disease the virus causes was named COVID-19; the COVID-19 pandemic
has changed the world today, becoming one of the major health challenges of the 21st
century. Having influenced many areas of life, it has changed the lives of many people
around the world. Almost two years after its beginning, it also became the subject of sci-
Citation: Rząsa, K.; Ciski, M.
Influence of the Demographic,
Social, and Environmental Factors
on the COVID-19 Pandemic—
Analysis of the Local Variations
Using Geographically Weighted
Regression. Int. J. Environ. Res.
Public Health 2022, 19, 11881.
Academic Editors: Zhengchao Dong,
Juan Manuel Gorriz and
Received: 19 August 2022
Accepted: 16 September 2022
Published: 20 September 2022
Publisher’s Note: MDPI stays neu-
tral with regard to jurisdictional
claims in published maps and institu-
Copyright: © 2022 by the authors. Li-
censee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and con-
ditions of the Creative Commons At-
tribution (CC BY) license (https://cre-
Int. J. Environ. Res. Public Health 2022, 19, 11881 2 of 28
Most of the studies that have been conducted and published focus on the medical
aspects of the COVID-19 pandemic: the effects on human health [4–12], as well as the pro-
cess and impact of vaccination [13–19]. The researchers analyzed the impact of the
COVID-19 pandemic on lifestyle behaviors and mental health; the pandemic is negatively
impacting self-perceived physical and mental health, particularly among people living
with non-communicable diseases [20–24]. Public health initiatives are needed to address
healthy lifestyle behaviors during and after the COVID-19 pandemic. A possible mitiga-
tion strategy for improving mental health includes taking suitable amounts of daily phys-
ical activity and sleeping well. The COVID-19 outbreak has reduced people’s aggressive-
ness, probably by making people realize the fragility and preciousness of life .
Many studies have attempted to determine the impact of the COVID-19 pandemic
on various areas of life. The COVID-19 pandemic, through a series of introduced re-
strictions and limitations, has changed the way people use public space; this has become
particularly apparent in the case of green spaces [25–32]. Urban open space played a pos-
itive role in allowing people to stay connected with their neighbors, to feel reassured, and
to maintain or increase their physical activity levels during the pandemic. The reason for
visiting green spaces much more often during lockdown was to take care of one’s mental
well-being, as well as for recreation. The perception of public spaces has changed, and
their accessibility and uses have changed [33–36]. The COVID-19 pandemic has influ-
enced the functioning and planning of urban areas—with half of the globe forced into
lockdown, urban planners needed a new approach to the use of space in many aspects of
urban activity and population mobility [37–45]. Employment has a significant impact on
mental health, especially in the context of a global pandemic. The workplace is therefore
an important target at which efforts should be directed to manage the mental health issues
associated with the COVID-19 pandemic [46–48]. The COVID-19 pandemic has unmasked
the problems related to uncertainty about the economic and employment aspects; this is
critical for policy design and financial strategy and planning [49–56]. The real estate mar-
ket also transformed during the COVID-19 pandemic; many households are reconsidering
their housing needs as their homes have become substitutes for offices, schools, restau-
rants, and recreational facilities . Changes in energy production and a reduction in
fossil fuel consumption before and during the COVID-19 pandemic, as well as the reduced
traffic and industrial activity in 2020, can explain the lower tropospheric NO2 emission
The research related to the COVID-19 pandemic has used GIS tools in addition to a
range of statistical tools [59–66]. GIS technology is very useful for aspects of research re-
lated to the analysis of COVID-19, the spatial spread of the epidemic, and other related
aspects of the pandemic. Monitoring new cases using GIS spatial analysis can be very
useful for controlling the course of the pandemic.
While the impact of the COVID-19 pandemic on various aspects related to human
life has been analyzed by researchers around the world, less emphasis was put on analyz-
ing the influence of the various socio-environmental factors on the spread of the pan-
demic. The studies show that housing quality, living conditions, demographic status, and
occupation were strong factors influencing the spread of the pandemic, as well as the mor-
tality rate of the COVID-19 cases . More populous and densely populated places have
a higher risk of transmission of COVID-19, especially with the Delta variant as the domi-
nant circulating strain. Therefore, extra control measures should be instituted in highly
populated areas to control the spread . The temperature variation and humidity may
also be important factors affecting COVID-19 mortality ; mean temperature has a pos-
itive linear relationship with the number of COVID-19 cases when the temperature is be-
low 3 °C .
Int. J. Environ. Res. Public Health 2022, 19, 11881 3 of 28
The purpose of this article is to analyze the influence of the socio-environmental fac-
tors on the spread of the COVID-19 pandemic. The extent to which various demographic,
social, and environmental factors explain the number of cases of SARS-CoV-2 is explored.
Poland was selected as a case-study area. The study’s introduction is a description of the
variables analyzed and a spatial autocorrelation test. The obtained database contained in-
formation on various demographic, social, and environmental factors, as well as the num-
ber of cases of SARS-CoV-2. The influence of the collected factors on the expansion of the
COVID-19 pandemic in Poland was examined using the geographically weighted regres-
sion method (GWR), with ArcGIS Pro 2.9 software (by Esri, Redlands, CA, USA). GIS tools
are widely used in various types of spatial analysis [71–73].
The stated research question was to investigate the influence of non-medical factors
(demographic, social, and environmental) on the COVID-19 pandemic in Poland. There-
fore, the following research hypothesis was established: using advanced statistical mod-
els, it is possible to indicate the influence of non-medical factors on the development of a
pandemic. An additional hypothesis of the article is the following: with the local variances
of the results obtained with the GWR method, it is possible to indicate anomalies in rela-
tion to the overall, national results, which can be a very useful basis for taking additional
measures by local authorities in the further fight against the COVID-19 pandemic.
The results of the conducted research expand the relatively small knowledge in this
area and can be used in further efforts to prevent the spread of the COVID-19 pandemic,
which is still active worldwide.
2. Materials and Methods
2.1. Study Area
Research on the influence of various demographic, social, and environmental factors,
as well as the number of cases of SARS-CoV-2, was carried out for the area of Poland. As
the level of accuracy, the counties were chosen—this is the second level of the administra-
tive division of the country (the first is the voivodeships, the third is the municipalities).
Poland is divided into 380 counties, with an average area of approximately 822 square
kilometers. Among the counties, there are 66 cities with county rights—these are munici-
palities with city status, executing county duties. Swietochlowice city county is the small-
est county (13 square kilometers), and Bialystok county is the largest (2975 square kilome-
The chosen level of accuracy of the study will allow a thorough examination of the
phenomenon (due to the relatively small area of the analyzed polygons) while maintain-
ing a higher administrative level, which brings greater data availability. In addition, the
GWR method chosen for the study requires at least 20 features to compute the results and
achieves the best results with larger datasets . The number of counties in Poland meets
the requirements of the GWR method. Figure 1 presents the study areas—the county and
voivodeship boundaries in Poland.
Int. J. Environ. Res. Public Health 2022, 19, 11881 4 of 28
Figure 1. Study area. Source: own elaboration using ArcGIS Pro 2.9 by Esri.
2.2. Data Source and Processing
The basis of the study was a database containing the number of cases of SARS-CoV-
2 in the Polish counties and a variety of demographic, social, and environmental factors.
The chosen spatial extent of the analysis, i.e., the counties of Poland, is the most spatially
accurate level for which the data on the number of cases of SARS-CoV-2 in Poland have
been published. The number of cases of SARS-CoV-2 in the counties in Poland was ob-
tained from data collected from reports provided by the University of Warsaw, the Voi-
vodeship Sanitary and Epidemiological Stations, and the County Sanitary and Epidemio-
logical Stations and from materials obtained from requests for access to public infor-
mation, as well as those collected from reports provided by the Ministry of Health; the
database was published by Michal Rogalski and Konrad Kalemba [75–77]. This is a relia-
ble source of data, used by many scientific studies examining the issue of the COVID-19
pandemic in Poland [14,78–84]. The following, in Figure 2, presents the spatial distribu-
tion of the summary of the cases of SARS-CoV-2 in the counties of Poland, as of the end
Int. J. Environ. Res. Public Health 2022, 19, 11881 5 of 28
Figure 2. Summary of confirmed cases of SARS-CoV-2 in Polish counties. Source: own elaboration
using ArcGIS Pro 2.9 by Esri.
In order to examine the possible influences of the demographic, social, and environ-
mental factors on the COVID-19 pandemic in Poland, it was necessary to determine the
state of these factors before the outbreak of the pandemic (creating a depiction of the de-
mographic, social, and environmental state of Poland as of December 2019). This is what
dictated the choice of the time range of the variables. The first cases of SARS-CoV-2 in
Poland were registered in March 2020; to study the phenomenon as widely as possible,
case data from two consecutive years (cases from the beginning of the pandemic in 2020
to 31 December 2021) were used.
All the data concerning the selected factors came directly from Statistics Poland, the
central office of government administration in Poland, which collects and shares statistical
information on the national level. This data source is reliable and provided by a public
institution; it is used in various scientific studies .
The research indicating the state of the study area in terms of demographic, social,
and environmental conditions often relies on a very broad set of indicators. It is related to
the purpose of the research, the thematic and spatial scope, and the level of detail. In the
research conducted within this article, the biggest limitation in the selection of indicators
was the level of spatial detail of the analyses (the research was conducted for the second
level of Poland’s administrative division). Most of the data published in Statistics Poland
are for the national or voivodeship level (the first level of administrative division). The
characteristics of the study required greater detail in the data, which limited the choice of
specific indicators the most.
Considering the above limitations, based on the analysis of the literature, a number
of demographic, social, and environmental indicators were selected for the research con-
ducted in this article [86–95]. The following in Table 1 lists the selected factors (variables),
broken down by thematic sections. In order to make the content and results more reada-
ble, each variable was attributed with a symbol, consisting of the first letter of the section’s
name and the subsequent ordinal number (e.g., the variable “Households receiving com-
munity social assistance” is located in the “Social” section and is the fourth variable; so, it
received the symbol “S4”). Replacing the variable name with a symbol will increase the
readability of the content; the symbolic designation is retained in the rest of the article.
Int. J. Environ. Res. Public Health 2022, 19, 11881 6 of 28
The full database used for the research in this article can be found in the Supplementary
Table 1. Selected demographic, social, and environmental factors. Source: own elaboration on the
basis of data from Statistics Poland.
Section Factor (Variable) Symbol
Total population D1
Urban population D2
Rural population D3
Population age: under 16 D4
Population age: 16–25 D5
Population age: 25–55 D6
Population age: over 55 D7
Number of beds in general hospitals S1
Physicians (total working staff) per 10,000 population
Nurses and midwives per 10,000 population S3
Households benefiting from community social assistance
according to the criterion of income S4
Families receiving family benefits for children S5
Families with assistance on the basis of poverty S6
Benefit payments from the 500+ program S7
Emission of air pollutants—particulates
Emission of air pollutants—gases E2
Forest cover E3
Share of parks, greens, and neighborhood green areas E4
The data on the structure of the population in the counties were gathered in Statistics
Poland from the National Population Censuses; the data include changes resulting from
births and deaths, as well as the migration of the population (for permanent residence and
for temporary stay) and changes caused by administrative changes. Data on the place of
residence of the population were collected on the basis of the PESEL register. The “Demo-
graphic” section is a detailed cross-section through the population, distinguishing place
of residence (urban–rural) and broken down into four age classes.
The “Social” section can be divided into two thematic groups: variables S1–S3 de-
scribe the level of healthcare and medical services, and variables S4–S7 identify areas bur-
dened by social problems resulting primarily from the economic exclusion of the popula-
tion. The state of health care, represented by the number of beds in hospitals, the number
of doctors, and the number of nurses, is obtained from the Center for Health Information
Systems, which is the institution responsible for the operation of the register of the entities
performing medical activities in Poland. The areas burdened by the economic exclusion
of the population were determined on the basis of the indicators of the living conditions
of the population: families benefiting from community social assistance and families with
assistance on the basis of poverty, as well as social welfare: families receiving family ben-
efits for children and payments from the government child-raising benefit 500+ program.
The “Environmental” section can also be divided into two thematic groups. The var-
iables E1 and E2 describe the state of environmental pollution through the magnitude of
the release of dust and gaseous pollutants into the atmosphere in an organized (through
stationary point sources) or unorganized manner (from dumps, landfills, during reload-
ing of loose or volatile substances, through roof and window ventilation, due to forest
fires, etc.). These are the emissions whose concentration exceeds the average content of
these substances in the clean air, negatively impacting human health and the condition
and quality of the environment. Variables E3 and E4 describe the country’s forest cover,
Int. J. Environ. Res. Public Health 2022, 19, 11881 7 of 28
as well as the areas covered with vegetation, located in the villages of dense buildings or
cities and used for aesthetic, recreational, therapeutic, or shielding purposes, in particular
parks, lawns, promenades, boulevards, botanical and zoological gardens, children’s play-
grounds, historic gardens, cemeteries, or other green areas located in the built-up areas.
Tabular data containing the number of cases of SARS-CoV-2 in the counties of Poland
and a variety of demographic, social, and environmental factors were merged and com-
bined with the polygon data representing the administrative boundaries of the counties
of Poland. The polygon data were obtained from the Polish National Register of Bounda-
ries (NRB) . The NRB is an official Polish spatial database, which forms the foundation
for other spatial information systems and shares data concerning the administrative units
of the country. The NRB covers the area of the whole country and contains information
on the boundaries and basic attributes of the three-tier administrative division of the coun-
try (i.e., municipalities, counties, and voivodeships). Thus, the obtained geodatabase was
used for further research. The following, in Figure 3, presents the spatial distribution of
the used variables in the counties of Poland.
Int. J. Environ. Res. Public Health 2022, 19, 11881 8 of 28
Int. J. Environ. Res. Public Health 2022, 19, 11881 9 of 28
Int. J. Environ. Res. Public Health 2022, 19, 11881 10 of 28
Figure 3. Spatial distribution of the explanatory variables in counties of Poland. Source: own elabo-
ration using ArcGIS Pro 2.9 by Esri.
2.3. Research Method
Prior to the study, the authors decided to establish the null hypothesis—there is no
statistical significance in all of the analyzed variables (in the case of this research—in the
various demographic, social, and environmental factors, as well as in the number of cases
of SARS-CoV-2 in the counties of Poland). The purpose of the null hypothesis is to test for
statistical significance in the data and to verify that the results obtained are not the result
of random chance. In order to assess the probability of the null hypothesis being true or
false, a spatial autocorrelation analysis (Global Moran’s I) was carried out using the
ArcGIS Pro 2.9 software (by Esri, Redlands, CA, USA). Spatial autocorrelation is the pres-
ence of systematic spatial variation in a variable; positive spatial autocorrelation is the
tendency for areas or places close together to have similar values. The most common
method for measuring spatial autocorrelation is to calculate the Moran’s I index [97–101].
The hypothesis test is as follows: if the Moran’s I = 0, there is no spatial autocorrelation; if
the Moran’s I > 0, spatial autocorrelation exists, with positive and negative values indicat-
ing positive and negative autocorrelation.
Once the null hypothesis is rejected, it is possible to proceed to an examination of the
influence of the selected variables on the number of cases of SARS-CoV-2 in the Polish
counties using geographically weighted regression. Geographically weighted regression
(GWR) is a method of analyzing spatial data based on regression, developed by adding
local spatial weights [102–106]. GWR models considerably improve the modeling fit by
capturing spatial heterogeneity, which is not factored into other regression models
[101,106]. This model fully aligns with the first law of geography. The GWR model can be
= (,)+ (,) +
—dependent variable at location i
—explanatory variable at location i
(,)—coordinate for location i
(,)—intercept location i
(,)—coefficient for explanatory variable k at location i
—residual location i
Int. J. Environ. Res. Public Health 2022, 19, 11881 11 of 28
The geographically weighted regression method has found application in studies re-
garding various infectious diseases: COVID-19 [107–109], AIDS , the Zika virus
[111,112], tuberculosis , malaria , and others [115,116]. The GWR method can be
used to estimate the effects of explanatory variables (in the case of this study, a number of
social, demographic, and environmental factors) on the dependent variable (number of
cases of SARS-CoV-2 in Polish counties) and also to identify the counties in which the
influence of the variables differs and to explore and interpret the spatial non-stationarity.
The ArcGIS Pro 2.9 software (by Esri, Redlands, CA, USA) was used to perform the GWR
In order to estimate the influence of the explanatory variables on the dependent var-
iable, the R2 parameter was used. R2 (or R-squared) is the proportion of the dependent
variable variance accounted for by the regression model; it is a measure of the goodness
of fit and quantifies the performance of a local GWR model. R2 is called the coefficient of
determination; its value varies from 0.0 to 1.0, and a higher value means that the explan-
atory variable explains the dependent variable better [99,117].
In order to evaluate whether or not the GWR model is biased by other factors, the
spatial autocorrelation (global Moran’s I) tool was performed on the regression residuals.
An unbiased model has residuals that are randomly scattered [106,118,119]. The explana-
tory variable explains the dependent variable well only after two requirements are met: a
high value of the coefficient of determination (e.g., R2 > 0.8) as well as the weak or insig-
nificant level of spatial autocorrelation in the residuals .
3.1. Spatial Autocorrelation Analysis
In order to evaluate the likelihood that the null hypothesis is true or false, spatial
autocorrelation (global Moran’s I) analysis was performed using ArcGIS Pro 2.9 software
(by Esri, Redlands, CA, USA). The spatial autocorrelation (global Moran’s I) tool measures
spatial autocorrelation on the basis of feature locations and feature values; it assesses
whether the expressed pattern is clustered, dispersed, or random. Below (Figure 4) are the
spatial autocorrelation (global Moran’s I) reports of the analyzed variables.
Number of cases of SARS-CoV-2 in Polish counties
Int. J. Environ. Res. Public Health 2022, 19, 11881 12 of 28
D4—Population age: under 16 D5—Population age: 16–25 D6—Population age: 25–55
D7—Population age: over 55 S1—Number of beds in general
S2—Physicians (total working
staff) per 10,000 population
S3—Nurses and midwives per
S4—Households benefiting from
community social assistance ac-
cording to the criterion of in-
S5—Families receiving family
benefits for children
Int. J. Environ. Res. Public Health 2022, 19, 11881 13 of 28
S6—Families with assistance on
the basis of poverty
S7—Benefit payments from the
E1—Emission of air pollutants—
E2—Emission of air pollutants—
E3—Forest cover E4—Share of parks, greens, and
neighborhood green areas
Figure 4. Spatial autocorrelation reports. Source: own elaboration using ArcGIS Pro 2.9 by Esri.
A positive z-score indicates a tendency to form clusters, and it has a negative aspect—
a tendency for the data to be dispersed [100,101]. The following, in Table 2, summarizes
the results of the spatial autocorrelation, with the p-value, the z-score, an indication of the
spatial pattern, and the confidence level for all the analyzed variables.
Table 2. Summary of the spatial autocorrelation. Source: own elaboration on the basis of ArcGIS Pro
2.9 by Esri.
Z-Score Spatial Pat-
Dependent COVID-19 cases 0.00 5.63 Clustered 1%
D1—Total population 0.00 4.00 Clustered 1%
D2—Urban population 0.01 2.62 Clustered 1%
D3—Rural population 0.00 −3.94 Dispersed 1%
D4—Population age: under 16
D5—Population age: 16–25 0.00 4.86 Clustered 1%
D6—Population age: 25–55 0.00 4.19 Clustered 1%
D7—Population age: over 55 0.01 2.79 Clustered 1%
S1—Number of beds in general hospitals 0.00 3.46 Clustered 1%
S2—Physicians (total working staff)
per 10,000 population 0.00 −3.04 Dispersed 1%
S3—Nurses and midwives per 10,000 population
S4—Households benefiting from community social as-
sistance according to the criterion of income 0.00 2.95 Clustered 1%
S5—Families receiving family benefits for children 0.00 6.30 Clustered 1%
S6—Families with assistance on the basis of poverty 0.00 3.29 Clustered 1%
Int. J. Environ. Res. Public Health 2022, 19, 11881 14 of 28
S7—Benefit payments from the 500+ program 0.00 6.24 Clustered 1%
E1—Emission of air pollutants—particulates 0.02 2.41 Clustered 5%
E2—Emission of air pollutants—gases
E3—Forest cover 0.00 10.41 Clustered 1%
E4—Share of parks, greens,
and neighborhood green areas 0.00 3.96 Clustered 1%
The obtained indicators made it possible to reject the original null hypothesis. Sixteen
out of eighteen variables are statistically significant at the 0.01 confidence level, and two
variables are statistically significant at the 0.05 confidence level. The positive z-scores in-
dicate the variables that are mostly spatially clustered, and the data are characterized by
spatial heterogeneity. This allows the research to continue; the next step was to assess the
influence of the selected variables on the number of cases of SARS-CoV-2 in Polish coun-
ties using geographically weighted regression.
3.2. Influence of the Selected Variables on the Number of Cases of SARS-CoV-2
The analysis was performed using ArcGIS Pro 2.9 software (by Esri, Redlands, CA,
USA). The polygon data representing the county boundaries in Poland were used for the
study. The tabular data containing the selected variables were combined with the polygon
data representing the polish counties, using the ‘Joins and Relates’ tool. Using the col-
lected explanatory variables, the GWR analysis of the influence of the variables on the
number of cases of SARS-CoV-2 in the Polish counties was carried out. The indicator val-
ues for the selected variables, as well as a statistical description of the variables, are shown
in Table 3 below.
Table 3. R2 and statistical description of the variables. Source: own elaboration on the basis of
ArcGIS Pro 2.9 by Esri.
Variable R2 Mean Min Max STD
D1—Total population 0.99 ** 101,006.8 19,914.0 1,790,658.0 119,730.5
D2—Urban population 0.95 ** 60,613.3 0.0 1,790,658.0 122,692.5
D3—Rural population 0.37 ** 40,393.4 0.0 264,014.0 35,060.6
D4—Population age: under 16 0.98 ** 16,433.6 2855.0 301,697.0 19,744.3
D5—Population age: 16–25 0.97 ** 9143.9 2054.0 112,725.0 8220.1
D6—Population age: 25–55
D7—Population age: over 55 0.98 ** 31,858.4 6623.0 578,722.0 39,298.0
S1—Number of beds in general hospitals 0.91 ** 439.0 0.0 11,970.0 907.7
S2—Physicians (total working staff)
per 10,000 population 0.73 ** 41.1 2.0 204.9 30.4
S3—Nurses and midwives
per 10,000 population 0.66 ** 60.6 2.4 237.4 37.5
S4—Households benefiting from community social assis-
to the criterion of income
0.94 ** 2171.1 472.0 20,186.0 1698.4
S5—Families receiving family benefits
for children 0.86 ** 2653.0 352.0 14,260.0 1739.9
S6—Families with assistance on the basis
of poverty 0.91 ** 1139.2 145.0 10,765.0 885.6
from the 500+ program 0.97 ** 80,276,961.1 15,183,262.0 1,366,004,134.0 90,144,295.6
E1—Emission of air pollutants—particulates 0.72 * 71.3 0.0 1924.0 143.3
E2—Emission of air pollutants—gases 0.70 * 522,212.5 0.0 32,882,772.0 2,138,844.5
E3—Forest cover 0.02 ** 26.0 0.0 70.4 13.4
E4—Share of parks, greens,
and neighborhood green areas 0.83 ** 0.8 0.0 20.9 1.9
Note: **—statistically significant at the p < 0.01 level; *—statistically significant at the p < 0.05 level.
Int. J. Environ. Res. Public Health 2022, 19, 11881 15 of 28
Among the analyzed variables, variable D1, the “total population,” is characterized
by the highest R2 value. All variables in the “Demographic” section exhibit a very high R2
value, with the exception of variable D3 “rural population”. These results indicate that the
spread of the COVID-19 pandemic took place primarily in cities; it can be closely associ-
ated with densely populated areas. The differences between the R2 values for the four age
divisions (variables D4–D7) are marginal but indicate that young people (in the 16–25 age
range) have a slightly smaller influence on the development of the COVID-19 pandemic
The variables in the “Social” section also had a strong influence on the dependent
variable: for variables S1, S4, S5, S6, and S7, the value was R2 > 0.80 (the highest value was
obtained by the variable S7 “Benefit payments from the 500+ program”), while with vari-
ables S2 and S3 a smaller impact was observed, at R2 = 0.73 and R2 = 0.66, respectively.
Variables S1-S3 determine the level of health care in the studied counties, while variables
S4–S7 reflect the level of social welfare and thus identify the areas burdened by social
problems resulting primarily from poverty. The areas most affected by these social prob-
lems appear to be the most susceptible to SARS-CoV-2.
The variables in the “Environmental” section did not appear to significantly influ-
ence the number of cases of SARS-CoV-2; only variable E4 has a significant R2 = 0.83.
3.3. Local R2 estimates
GWR allows for the exploration of spatially varying relationships. In order to esti-
mate the spatial distribution of different variables, the local R2 estimates are presented
below (Figures 5–7). This is a way to visualize how the relationships between the explan-
atory variables and the dependent variable vary across the study area. Mapping the local
R2 estimates may provide clues about important variables that may be missing from the
regression model; for this study, the focus was on whether or not the number of cases of
SARS-CoV-2 in Polish counties was influenced by factors other than the examined varia-
Selecting the appropriate data classification and symbolization method is an ex-
tremely important part of the cartographic process . The class selection was carried
out using the Jenks natural breaks method. This method is one of the most popular data
classification methods [85,121,122]; it is used to reduce the variance within classes and to
maximize the variance between classes. The comparison of the content of the maps is only
possible if equal classification (division into classes) is used . To enable the compari-
son of the local R2 distribution for all the explanatory variables in all three sections, the
classification of the maps was standardized. This way, each class on each map corre-
sponds to the same values; this allowed us to draw valid conclusions without any spatial-
The boundaries of the voivodeships were overlaid on the maps; the voivodeships do
not have full autonomy, but they do have some administrative independence, primarily
in terms of decision making and the disposition of resources. Such a procedure allowed
for additional analysis of the impact of voivodeship government actions on the progress
of the COVID-19 pandemic in Poland. The following, in Figure 5, presents the local R2
values for the “Demographic” section.
Int. J. Environ. Res. Public Health 2022, 19, 11881 16 of 28
Int. J. Environ. Res. Public Health 2022, 19, 11881 17 of 28
Figure 5. Local R2 estimates for the “Demographic” section. Source: own elaboration using ArcGIS
Pro 2.9 by Esri.
The use of local R2 prediction maps revealed a significant variance in the demo-
graphic variables analyzed in the study area. The majority of the counties in the Masovian
voivodeship have the highest local R2 values for all the studied variables. For the majority
of variables, the counties in the southwestern part of Poland indicate a very large devia-
tion from the R2 value for the country. By overlaying a layer of the administrative bound-
aries of the voivodeships, in the case of the demographic data, a significant effect of the
location of the counties in a given voivodeship on the local variance of the R2 parameter
can be observed (this is evident when the administrative boundary of a voivodeship over-
laps with counties belonging to two different symbolization classes). Within the bounda-
ries of a single voivodeship, the counties are often in up to two (and sometimes even one)
In the case of the explanatory variable D1 (total population), the counties located in
the West Pomeranian, Greater Poland, Łódź, and Masovian voivodeships appear to ex-
plain the explanatory variable the best (the local R2 values are in the fifth class, which is
the highest); on the other hand, the counties located in southwestern Poland explain the
explanatory variable the least effectively. The spatial distribution of the local R2 for the D2
variable almost matches that for the D1 variable. The map of the D3 variable shows an
unusually large number of counties for which the local R2 values are negative or close to
zero. This indicates a low level of explanation of the dependent variable, which is con-
firmed by the overall R2 for this explanatory variable (R2 = 0.37). Examining the age struc-
ture of the population (four variables D4–D7), it is noticeable how the results obtained are
not significantly different from the results obtained for variable D1. For the D6 variable,
which best explains the dependent variable (R2 = 0.99), no county is characterized with a
negative local R2.
The following, in Figure 6, shows the local R2 values for the “Social” section. The
bottom range of the lowest class was slightly modified to account for the decreasing local
Int. J. Environ. Res. Public Health 2022, 19, 11881 18 of 28
Int. J. Environ. Res. Public Health 2022, 19, 11881 19 of 28
Figure 6. Local R2 estimates for the “Social” section. Source: own elaboration using ArcGIS Pro 2.9
In the “Social” section, two thematic groups of explanatory variables can be distin-
guished: variables S1–S3 describe the level of healthcare and medical services, and varia-
bles S4–S7 identify the areas burdened by social problems resulting primarily from the
economic exclusion of the population. For both thematic groups, the local R2 maps show
far greater heterogeneity. Most of the counties in the Masovian voivodeship for almost all
the explanatory variables seem to best explain the dependent variable. In addition, again
the regions of southwestern Poland and the eastern counties of the West Pomeranian voi-
vodeship explain the dependent variable the least.
In the case of the first thematic group, one can see the preservation of spatial rela-
tionships between the local R2 values. The areas indicating a lower value of local R2 appear
to be almost the same across all three maps. It is most noticeable in the following areas:
the eastern part of the West Pomeranian voivodeship and the southwestern part of the
country, as well as almost all of the Holy Cross and Masovian voivodeships. The de-
scribed counties might belong to different symbolization classes, but their mutual rela-
tionship is preserved. A similar pattern can also be observed in similar areas for the sec-
ond thematic group, especially in the counties of the Masovian and West Pomeranian voi-
As in the case of the “Demographic” section, one can see a large impact of the location
of the counties in a given voivodeship on the local variance of the R2 parameter. For in-
stance, this is clearly seen in the case of explanatory variables S6 and S7.
The following, in Figure 7, illustrates the local R2 values for the variables in the “En-
vironmental” section. The bottom range of the lowest class was again slightly modified to
account for the further decreasing local R2 value.
Int. J. Environ. Res. Public Health 2022, 19, 11881 20 of 28
Figure 7. Local R2 estimates for the “Environmental” section. Source: own elaboration using ArcGIS
Pro 2.9 by Esri.
The maps of the local R2 of the “Environmental” section demonstrate the highest het-
erogeneity overall by far, higher than the other two sections. In addition, compared to the
other sections, more counties show negative local R2 values. The highest local R2 values
were again obtained in the counties of the Masovian voivodeship. Negative values of local
R2 appear throughout the country but again dominate in the southwestern part of the
The map of the local R2 of the E3 variable “forest cover” strongly deviates from the
rest of the results. In this particular case, the authors were forced to use a different classi-
fication of the data since the local R2 values are in the range of 0.013–0.026. Applying the
same classification as for the other local R2 maps would place the entire case study area in
a single class of 0–0.3. This situation is due to the extremely low R2 value for the entire
variable, which is 0.02.
Int. J. Environ. Res. Public Health 2022, 19, 11881 21 of 28
3.4. Residual Spatial Autocorrelation
In order to evaluate the performance of the GWR model, the existence of residual
spatial autocorrelation was analyzed. The spatial autocorrelation (global Moran’s I) test
was again performed using the ArcGIS Pro 2.9 software (by Esri, Redlands, CA, USA) to
measure the residual spatial autocorrelation. The results are shown in Table 4 below.
Table 4. Spatial autocorrelation results for GWR residuals. Source: own elaboration on the basis of
ArcGIS Pro 2.9 by Esri.
D1 D2 D3 D4 D5 D6 D7 S1 S2 S3 S4 S5 S6 S7 E1 E2 E3 E4
pattern R R R wD R wD wD R R R R R R wD
R R R R
Note: R—random; wD—weak dispersed.
Given the z-scores, the pattern of residuals does not appear to be significantly differ-
ent than the random. Only for variables D4, D6, D7, and S7 were values indicating a weak
dispersed pattern recorded (z-score between −1.96 and −1.65). The results indicate the ran-
dom nature of the residuals, confirming the validity of using the GWR model.
In the available databases of academic articles, the influence of the COVID-19 pan-
demic on various aspects related to human life has been quite thoroughly analyzed. Less
emphasis was put on analyzing the influence of the various socio-environmental factors
on the spread of the pandemic. The results of this research expanded the relatively small
knowledge on the impact of the various factors on the progress and course of the COVID-
The conducted research confirmed the research hypotheses. The established goal of
the research, which was to assess the influence of various demographic, social, and envi-
ronmental factors on the number of cases of SARS-CoV-2, was implemented using the
geographical weighted regression (GWR) method. R2 parameter values, or the coefficients
of determination, were compiled; the values vary from 0.0 to 1.0, and a higher value means
that the explanatory variable explains the dependent variable better. For this research, this
means that the higher the R2 value for a demographic, social, or environmental variable,
the better that variable explains the number of cases of SARS-CoV-2.
The highest values of the R2 parameter were obtained for the variables in the “Demo-
graphic” section; the values for most of these variables ranged from 0.97 to 0.99. This con-
firms that population and the associated higher amount and intensity of human contact
are the most influential factors in the development of the COVID-19 pandemic. The low
R2 = 0.37 result for variable D3 “rural population” also confirms this point. The rural areas
are characterized by a smaller population and a greater degree of dispersion, which im-
plies fewer person-to-person interactions. The rural areas also tend to have reduced pop-
ulation mobility, which may restrict the spread of the virus.
The “Social” section can be subdivided into two thematic groups: the S1–S3 variables
describe the level of health care and medical services, and the S4–S7 variables identify the
areas burdened by the economic exclusion of the population. The R2 parameter for the
variables in the “Social” section obtains values at an average level, in the range of 0.66–
0.97. This is still a high degree of explanation of the dependent variable but clearly lower
than the variables in the “Demographic” section. The highest R2 value in this section was
recorded for variable S1—number of beds in general hospitals. This may be related to the
fact of the increased number of healthcare facilities in urban areas, where a higher number
of cases of SARS-CoV-2 virus infection have occurred. Negative R2 values were obtained
in the counties of southwestern Poland: Bolesławiec county, Zgorzelec county, and Żagań
Int. J. Environ. Res. Public Health 2022, 19, 11881 22 of 28
county. The R2 values for variables S2 and S3, although thematically linked to variable S1,
are lower, at 0.73 and 0.66, respectively. Accordingly, the number of medical personnel
explains the dependent variable to a lesser extent. The areas indicating a negative value
of local R2 appear across all the maps. The thematic group of the variables S3–S7, indicat-
ing the areas burdened by social problems resulting primarily from the economic exclu-
sion of the population, is characterized by a high level of the R2 parameter. This strongly
implies that the poverty-burdened areas are also highly vulnerable to the development of
the COVID-19 pandemic.
The lowest R2 values (in the range of 0.02 to 0.83) for the “Environmental” section
variables indicate a lower degree of explanation for the SARS-CoV-2 cases. The highest R2
value in this section was registered for variable E4, i.e., the share of parks, greens, and
neighborhood green areas. This is another explanatory variable that is strongly associated
with urban areas, which may explain the obtained result. The variables associated with
the environmental pollution issues (E1 and E2) moderately explain the number of cases of
SARS-CoV-2 infection. The R2 for these variables is 0.72 and 0.70, respectively. The lowest
value of the R2 parameter was recorded for variable S3, i.e., forest cover—the obtained
level of R2 = 0.02 indicates a complete lack of association of this variable with the depend-
Thus, the results showed that the demographic factors best explained the number of
cases of SARS-CoV-2, the social factors explained it to an average degree, and the envi-
ronmental factors explained it to the lowest degree.
The application of the GWR method is not common in COVID-19 pandemic studies.
In addition, most the research of this type is carried out at the country level. In the research
conducted in the article, the authors developed maps of local R2 at a high level of spatial
detail (research at the level of Poland’s second administrative division), allowing for more
detailed conclusions and the formulation of more specific guidelines for local authorities.
The GWR method allowed for the exploration of spatially varying relationships; us-
ing maps of the local R2, it was possible to visualize how the relationships between the
explanatory variables and the dependent variable vary across the study area. Considering
that the COVID-19 pandemic is still ongoing, and more waves are being projected, the
results obtained may be useful for local authorities in developing strategies to counter the
pandemic. This is especially true for counties for which the GWR method has been able
to identify deviations from the results obtained for the country as a whole. These counties
can be regarded as a kind of anomaly, requiring specific, non-standard measures. Such a
situation can be seen, for example, in Koszalin county, and Koszalin city county, Sławno
county, and Szczecinek county (in the West Pomeranian voivodeship); Gorlice county in
the Lesser Poland voivodeship; Jasło county in the Subcarpathian voivodeship; and
Mława county, Ostrołęka county, Ostrów county, Płock county, Płock city county,
Przasnysz country, Sierpc county, and Żuromin county (in the Masovian voivodeship), as
well as the southwestern part of Poland, primarily Bolesławiec county, Lubań county,
Lwówek county, Zgorzelec county, and Żagań county.
Similarly, the results of the local R2 for the variables indicating areas burdened with
social problems resulting primarily from the economic exclusion of the population may
be helpful to local authorities. The high association of the poverty problem with the spread
of the COVID-19 pandemic has been shown above. Such a situation can be seen in partic-
ular, for example, in the counties in the central Masovian voivodeship: Białobrzegi county,
Ciechanów county, Garwolin county, Grodzisk county, Grójec county, Kozienice county,
Legionowo county, Warsaw city county, Warsaw West county, Maków Mazowiecki
county, Mińsk Mazowiecki county, Nowy Dwór Mazowiecki county, Otwock county, Pi-
aseczno county, Płońsk county, Pruszków county, Pułtusk county, Sochaczew county,
Węgrów county, Wołomin county, Wyszków county, and Żyrardów county.
Int. J. Environ. Res. Public Health 2022, 19, 11881 23 of 28
In-depth analysis combining the map of the SARS-CoV-2 cases, the maps of the indi-
cator values, and the maps of the local R2 enabled the detection of counties where the local
authorities should take specific measures, targeting the further fight against the COVID-
19 pandemic in Poland. As an example, the following counties can be mentioned: Biała
Podlaska county (Lubusz voivodeship), Koszalin county (West Pomeranian voivodeship),
Warsaw city county, Kozienice county (Masovian voivodeship).
Biala Podlaska county is characterized by a negative R2 for the S2 variable “Physi-
cians (total working staff) per 10,000 population”; from the “Social” section, there is a very
high number of SARS-CoV-2 cases, while the number of working medical staff is consid-
erably low. The present study indicates the need to increase the medical staff in this
county in order to more effectively combat the COVID-19 pandemic. A similar situation
(although to a lesser extent) is present in Koszalin county (West Pomeranian voivodeship).
In the “Environmental” section, many instances are noticeable where high levels of
air pollution (represented by variables E1 and E2) largely explain the high number of cases
of SARS-CoV-2. This is particularly evident in the counties of Warsaw city county, or
Kozienice county (Masovian voivodeship). Warsaw, as the country’s capital, is particu-
larly exposed to air pollution. Kozienice, the capital of Kozienice county, is the location of
the second largest coal-fired power station in Poland. The largest coal-fired power station
in Poland is located in Bełchatow county (Łódź voivodeship), which features distinctly in
the air pollution maps, but the local R2 maps do not indicate a large influence of this phe-
nomenon on the number of SARS-CoV-2 cases in this county; this may indicate that the
local authorities are working effectively in this regard.
Such a thorough analysis of the set of maps prepared in this article can become a
considerably useful tool for the authorities of a particular county, which could receive an
answer to the question of to what extent to take action to further combat the COVID-19
pandemic. This might allow the specifying of actions in a particular direction in order to
more effectively use the resources of the county or to confirm the effectiveness of the ac-
tions already being carried out. The proposed methodology is a ready-to-use tool for
county authorities in Poland, but it can also be used in any other country with the same
or greater level of spatial accuracy (lower level of administrative divisions). In this respect,
this is an important innovative element of the article, as most of the previous studies fo-
cused only on the national level.
Supplementary Materials: The following supporting information can be downloaded at:
www.mdpi.com/article/10.3390/ijerph191911881/s1; The full database of factors used for this re-
search available to download in the Supplementary Materials.
Author Contributions: Conceptualization, M.C. and K.R.; methodology, M.C. and K.R.; software,
M.C.; validation, K.R.; formal analysis, K.R.; investigation, M.C. and K.R.; resources, M.C. and K.R.;
data curation, M.C.; writing—original draft preparation, M.C. and K.R.; writing—review and edit-
ing, M.C.; visualization, M.C.; supervision, K.R.; project administration, K.R. All authors have read
and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest. The authors declare equal contri-
butions to the article.
Int. J. Environ. Res. Public Health 2022, 19, 11881 24 of 28
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